As consumer interaction with online resources (e.g., use of web resources, e-commerce, browsing activity, etc.) has grown, digital marketing and web analytics to make marketing decisions have also becoming increasingly more common. Generally, digital marketers seek to offer products, services, and content to consumers who will find the offers favorable and have a high probability of acting on the offers. Accordingly, one challenge faced by digital marketers is matching of offers to users so as to maximize the likelihood that users will accept the offers and accordingly optimize the return/reward to the digital marketers derived from the offers. Web analytics refers generally to tools that enable marketers to analyze consumer interaction and behaviors with web resources including for example creation of reports and interactive dashboards, real-time data manipulation and identification of issues, analysis of key performance indicators (KPIs), and assessment of opportunities.
Traditionally, web analytics platforms offer complex and/or proprietary workflows that may be difficult for unsophisticated users to utilize to produce custom reports and data views. Although, some platforms provide rudimentary support for access to analytics data through commercially available desktop applications (e.g., word processing and spreadsheet programs), the ability of analysts and marketers to manipulate data directly within such applications remains limited. Rather than being able to make modifications within familiar environments of desktop productivity applications, users are forced to make any changes to data views and dashboards directly through web analytics platforms, which may be time consuming and costly. Accordingly, analysis tools available through existing web analytics platforms may be insufficient for some users and data analysis scenarios.